Current Issue : January-March Volume : 2025 Issue Number : 1 Articles : 5 Articles
The primary objective of a depalletizing system is to automate the process of detecting and locating specific variable-shaped objects on a pallet, allowing a robotic system to accurately unstack them. Although many solutions exist for the problem in industrial and manufacturing settings, the application to small-scale scenarios such as retail vending machines and small warehouses has not received much attention so far. This paper presents a comparative analysis of four different computer vision algorithms for the depalletizing task, implemented on a Raspberry Pi 4, a very popular single-board computer with low computer power suitable for the IoT and edge computing. The algorithms evaluated include the following: pattern matching, scale-invariant feature transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifier. Each technique is described and their implementations are outlined. Their evaluation is performed on the task of box detection and localization in the test images to assess their suitability in a depalletizing system. The performance of the algorithms is given in terms of accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The results reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of a depalletizing system....
High-throughput screening (HTS) can be used when ab initio information is unavailable for rational design of new materials, generating data on properties such as chemistry and topography that control cell behavior. Biomaterial screens are typically fabricated as microarrays or “chips,” seeded with the cell type of interest, then phenotyped using immunocytochemistry and high-content imaging, generating vast quantities of image data. Typically, analysis is only performed on fluorescent cell images as it is relatively simple to automate through intensity thresholding of cellular features. Automated analysis of brightfield images is rarely performed as it presents an automation challenge as segmentation thresholds that work in all images cannot be defined. This limits the biological insight as cell response cannot be correlated to specifics of the biomaterial feature (e.g., shape, size) as these features are not visible on fluorescence images. Computer Vision aims to digitize tasks humans do by sight, such as identify objects by their shape. Herein, two case studies demonstrate how open-source approaches, (region-based convolutional neural network and algorithmic [OpenCV]), can be integrated into cell-biomaterial HTS analysis to automate bright-field segmentation across thousands of images, allowing rapid, spatial definition of biomaterial features during cell analysis for the first time....
The crack feature is the key to crack recognition in concrete surface crack detection systems based on machine vision. As the “essence” of crack, the crack skeleton shows good stability. Therefore, a feature extraction method based on inflection point recognition of concrete surface crack skeleton is proposed in this paper. The main processing steps include image preprocessing, crack skeleton extraction, and inflection point recognition. In extracting the crack skeleton, the skeleton is initially extracted using morphological operations. After identifying the endpoints based on the neighborhood distribution of the skeleton points, the burrs are tracked until the branching point using the endpoints as the starting point, and the skeleton burrs are judged and removed by setting a threshold with a burr removal rate of 100%. For the identification of inflection points based on chain code calculation, the algorithm is optimized by the concentration of the inflection points and the distance and angle between the inflection points to remove false inflection points. The test data show that the algorithm has good adaptability and the accuracy is higher than 90%. After obtaining the crack skeleton image with real inflection points, the structural features of the skeleton can be calculated, including the distance ratio before and after the inflection points and the angle formed between the inflection points. This lays the foundation for future research to realize the recognition of the same crack at different time points....
Color characteristics are a crucial indicator of green tea quality, particularly in needleshaped green tea, and are predominantly evaluated through subjective sensory analysis. Thus, the necessity arises for an objective, precise, and efficient assessment methodology. In this study, 885 images from 157 samples, obtained through computer vision technology, were used to predict sensory evaluation results based on the color features of the images. Three machine learning methods, Random Forest (RF), Support Vector Machine (SVM) and Decision Tree-based AdaBoost (DT-AdaBoost), were carried out to construct the color quality evaluation model. Notably, the DT-Adaboost model shows significant potential for application in evaluating tea quality, with a correct discrimination rate (CDR) of 98.50% and a relative percent deviation (RPD) of 14.827 in the 266 samples used to verify the accuracy of the model. This result indicates that the integration of computer vision with machine learning models presents an effective approach for assessing the color quality of needle-shaped green tea....
D-K-type bauxite from Guizhou can be used as an unburned ceramic, adsorbent, and geopolymer after low-temperature calcination. It aims to solve the problem where the color of the D–K-type bauxite changes after calcination at different temperatures. Digital image processing technology was used to extract the color characteristics of bauxite images after 10 min of calcination at various temperatures. Then, we analyzed changes in the chemical composition and micromorphology of bauxite before and after calcination and investigated the correlation between the color characteristics of images and composition changes after bauxite calcination. The test results indicated that after calcining bauxite at 500 ◦C to 1000 ◦C for 10 min, more obvious dehydration and decarburization reactions occurred. The main component gradually changed from diaspore to Al2O3, the chromaticity value of the image decreased from 0.0980 to 0.0515, the saturation value increased from 0.0161 to 0.2433, and the brightness value increased from 0.5890 to 0.7177. Studies have shown that changes in bauxite color characteristics are strongly correlated with changes in composition. This is important for directing bauxite calcination based on digital image processing from engineering viewpoints....
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